Merge branch 'dev' into master

1 files changed, 31 insertions, 6 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 6d9c6a8d..a48fff99 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -3,6 +3,7 @@ import math
 import os

 import sys

 import warnings

+import hashlib

 

 import torch

 import numpy as np

@@ -105,7 +106,7 @@ class StableDiffusionProcessing:
     """

     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing

     """

-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):

+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):

         if sampler_index is not None:

             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)

 

@@ -140,6 +141,7 @@ class StableDiffusionProcessing:
         self.denoising_strength: float = denoising_strength

         self.sampler_noise_scheduler_override = None

         self.ddim_discretize = ddim_discretize or opts.ddim_discretize

+        self.s_min_uncond = s_min_uncond or opts.s_min_uncond

         self.s_churn = s_churn or opts.s_churn

         self.s_tmin = s_tmin or opts.s_tmin

         self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option

@@ -162,6 +164,8 @@ class StableDiffusionProcessing:
         self.all_seeds = None

         self.all_subseeds = None

         self.iteration = 0

+        self.is_hr_pass = False

+        

 

     @property

     def sd_model(self):

@@ -476,6 +480,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,

         "Clip skip": None if clip_skip <= 1 else clip_skip,

         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,

+        "Init image hash": getattr(p, 'init_img_hash', None),

+        "RNG": opts.randn_source if opts.randn_source != "GPU" else None,

+        "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,

     }

 

     generation_params.update(p.extra_generation_params)

@@ -639,8 +646,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     processed = Processed(p, [], p.seed, "")

                     file.write(processed.infotext(p, 0))

 

-            uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)

-            c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)

+            step_multiplier = 1

+            if not shared.opts.dont_fix_second_order_samplers_schedule:

+                try:

+                    step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1

+                except:

+                    pass

+            uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)

+            c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)

 

             if len(model_hijack.comments) > 0:

                 for comment in model_hijack.comments:

@@ -670,6 +683,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)

 

             for i, x_sample in enumerate(x_samples_ddim):

+                p.batch_index = i

+

                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)

                 x_sample = x_sample.astype(np.uint8)

 

@@ -706,9 +721,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     image.info["parameters"] = text

                 output_images.append(image)

 

-                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:

+                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):

                     image_mask = p.mask_for_overlay.convert('RGB')

-                    image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), p.mask_for_overlay.convert('L')).convert('RGBA')

+                    image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')

 

                     if opts.save_mask:

                         images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")

@@ -718,7 +733,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 

                     if opts.return_mask:

                         output_images.append(image_mask)

-                    

+

                     if opts.return_mask_composite:

                         output_images.append(image_mask_composite)

 

@@ -871,6 +886,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         if not self.enable_hr:

             return samples

 

+        self.is_hr_pass = True

+

         target_width = self.hr_upscale_to_x

         target_height = self.hr_upscale_to_y

 

@@ -940,6 +957,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 

         samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)

 

+        self.is_hr_pass = False

+

         return samples

 

 

@@ -1007,6 +1026,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             self.color_corrections = []

         imgs = []

         for img in self.init_images:

+

+            # Save init image

+            if opts.save_init_img:

+                self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest()

+                images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False)

+

             image = images.flatten(img, opts.img2img_background_color)

 

             if crop_region is None and self.resize_mode != 3: